def evaluate(self, x, ytrue_deg, data_part, return_per_image=False):
ytrue_bit = deg2bit(ytrue_deg)
ypreds = self.model.predict(x)
results = dict()
vmmix_mu, vmmix_kappas, vmmix_probs = self.parse_output_np(ypreds)
vmmix_mu_rad = np.deg2rad(bit2deg_multi(vmmix_mu))
samples = sample_von_mises_mixture_multi(vmmix_mu_rad, vmmix_kappas, vmmix_probs, n_samples=100)
point_preds = maximum_expected_utility(np.rad2deg(samples))
maad_errs = maad_from_deg(point_preds, ytrue_deg)
results['maad_loss'] = float(np.mean(maad_errs))
results['maad_sem'] = float(sem(maad_errs))
log_likelihoods = self._von_mises_mixture_log_likelihood_np(ytrue_bit, ypreds)
results['log_likelihood_mean'] = float(np.mean(log_likelihoods))
results['log_likelihood_sem'] = float(sem(log_likelihoods, axis=None))
print("MAAD error (%s) : %f pm %fSEM" % (data_part,
results['maad_loss'],
results['maad_sem']))
print("log-likelihood (%s) : %f pm %fSEM" % (data_part,
results['log_likelihood_mean'],
results['log_likelihood_sem']))
if return_per_image:
results['point_preds'] = bit2deg(deg2bit(point_preds))
results['maad'] = maad_errs
results['log_likelihood'] = log_likelihoods
return results
def maad(self, y_true_deg, y_pred_deg, angle='', verbose=1):
aads = maad_from_deg(y_true_deg, y_pred_deg)
maad = np.mean(aads)
sem = stats.sem(aads)
if verbose:
print("MAAD %s : %2.2f+-%2.2fSE" % (angle, maad, sem))
return aads, maad, sem
def maad(self, y_true_deg, y_pred_deg, angle='', verbose=1):
""" Compute Mean Absolute Angular Deviation between ground truth and predictions (in degrees)
"""
aads = maad_from_deg(y_true_deg, y_pred_deg)
maad = np.mean(aads)
sem = stats.sem(aads)
if verbose:
print("MAAD %s : %2.2f+-%2.2fSE" % (angle, maad, sem))
return aads, maad, sem
def evaluate_multi(self, x, ytrue_deg, data_part, n_samples=50, verbose=1):
ytrue_bit = deg2bit(ytrue_deg)
results = dict()
preds = self.get_multiple_predictions(x,
ytrue_bit,
n_samples=n_samples)
results['elbo'] = np.mean(preds['elbo'])
results['elbo_sem'] = sem(np.mean(preds['elbo'], axis=1))
results['kl_div'] = np.mean(preds['kl_div'])
results['kl_div_sem'] = sem(np.mean(preds['kl_div'], axis=1))
ypreds = self.decoder_model.predict(x)
ypreds_bit = ypreds[:, 0:2]
kappa_preds = ypreds[:, 2:]
ypreds_deg = bit2deg(ypreds_bit)
loss = maad_from_deg(ytrue_deg, preds['maxutil_deg_dec'])
results['maad_loss'] = np.mean(loss)
results['maad_loss_sem'] = sem(loss, axis=None)
importance_loglikelihoods = importance_loglikelihood(
preds['mu_encoder'], preds['log_sigma_encoder'], preds['mu_prior'],
preds['log_sigma_prior'], preds['u_encoder'], preds['mu_bit'],
preds['kappa'], ytrue_bit)
results['importance_log_likelihood'] = np.mean(
importance_loglikelihoods)
results['importance_log_likelihood_sem'] = sem(
importance_loglikelihoods, axis=None)
if verbose:
print("MAAD error (%s) : %f ± %fSEM" %
(data_part, results['maad_loss'], results['maad_loss_sem']))
print("ELBO (%s) : %f ± %fSEM" %
(data_part, results['elbo'], results['elbo_sem']))
print(
"Approx Log-Likelihood, importance sampling (%s) : %f ± %fSEM"
% (data_part, results['importance_log_likelihood'],
results['importance_log_likelihood_sem']))
print("KL-div (%s) : %f ± %fSEM" %
(data_part, results['kl_div'], results['kl_div_sem']))
return results
def evaluate(self, x, ytrue_deg, data_part):
ypreds_deg = np.squeeze(self.model.predict(x))
loss = maad_from_deg(ypreds_deg, ytrue_deg)
results = dict()
results['maad_loss'] = float(np.mean(loss))
results['maad_loss_sem'] = float(sem(loss, axis=None))
print("MAAD error (%s) : %f ± %fSEM" %
(data_part, results['maad_loss'], results['maad_loss_sem']))
return results
def evaluate(self, x, ytrue_deg, data_part, verbose=1):
ytrue_bit = deg2bit(ytrue_deg)
results = dict()
cvae_preds = self.full_model.predict([x, ytrue_bit])
elbo, ll, kl = self._cvae_elbo_loss_np(ytrue_bit, cvae_preds)
results['elbo'] = np.mean(elbo)
results['elbo_sem'] = sem(elbo)
results['kl'] = np.mean(kl)
results['kl_sem'] = sem(kl)
results['log_likelihood'] = np.mean(ll)
results['log_likelihood_loss_sem'] = sem(ll)
ypreds = self.decoder_model.predict(x)
ypreds_bit = ypreds[:, 0:2]
kappa_preds = ypreds[:, 2:]
ypreds_deg = bit2deg(ypreds_bit)
loss = maad_from_deg(ytrue_deg, ypreds_deg)
results['maad_loss'] = np.mean(loss)
results['maad_loss_sem'] = sem(loss)
# log_likelihood_loss = von_mises_log_likelihood_np(ytrue_bit, ypreds_bit, kappa_preds,
# input_type='biternion')
# results['log_likelihood'] = np.mean(log_likelihood_loss)
# results['log_likelihood_loss_sem'] = sem(log_likelihood_loss)
if verbose:
print("MAAD error (%s) : %f ± %fSEM" %
(data_part, results['maad_loss'], results['maad_loss_sem']))
print("ELBO (%s) : %f ± %fSEM" %
(data_part, results['elbo'], results['elbo_sem']))
print("KL-div (%s) : %f ± %fSEM" %
(data_part, results['kl'], results['kl_sem']))
# print("log-likelihood (%s) : %f±%fSEM" % (data_part,
# results['log_likelihood'],
# results['log_likelihood_loss_sem']))
return results
def evaluate(self, x, ytrue_deg, data_part, return_per_image=False):
ytrue_bit = deg2bit(ytrue_deg)
ypreds = self.model.predict(x)
ypreds_bit = ypreds[:, 0:2]
ypreds_deg = bit2deg(ypreds_bit)
if self.predict_kappa:
kappa_preds = ypreds[:, 2:]
else:
kappa_preds = np.ones([ytrue_deg.shape[0], 1
]) * self.fixed_kappa_value
loss = maad_from_deg(ypreds_deg, ytrue_deg)
results = dict()
results['maad_loss'] = float(np.mean(loss))
results['maad_loss_sem'] = float(sem(loss))
print("MAAD error (%s) : %f pm %fSEM" %
(data_part, results['maad_loss'], results['maad_loss_sem']))
results['mean_kappa'] = float(np.mean(kappa_preds))
results['std_kappa'] = float(np.std(kappa_preds))
log_likelihoods = von_mises_log_likelihood_np(ytrue_bit, ypreds_bit,
kappa_preds)
results['log_likelihood_mean'] = float(np.mean(log_likelihoods))
results['log_likelihood_sem'] = float(sem(log_likelihoods, axis=None))
print("log-likelihood (%s) : %f pm %fSEM" %
(data_part, results['log_likelihood_mean'],
results['log_likelihood_sem']))
if return_per_image:
results['point_preds'] = bit2deg(deg2bit(ypreds_deg))
results['maad'] = loss
results['log_likelihood'] = log_likelihoods
return results